Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy
There is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing res...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X24000985 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850250285306347520 |
|---|---|
| author | Di Wu Meng Chen Xu Chen Xing Liu |
| author_facet | Di Wu Meng Chen Xu Chen Xing Liu |
| author_sort | Di Wu |
| collection | DOAJ |
| description | There is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing research on K-12 AI education relies on experiential methodologies and suffers from a lack of quantitative analysis based on extensive classroom data, hindering a comprehensive depiction of AI education's current state at these educational levels. To address this gap, this article employs the advanced semantic understanding capabilities of large language models (LLMs) to create an intelligent analysis framework that identifies learning theories, pedagogical approaches, learning tools, and levels of AI literacy in AI classroom instruction. Compared with the results of manual analysis, analysis based on LLMs can achieve more than 90% consistency. Our findings, based on the analysis of 98 classroom instruction videos in central Chinese cities, reveal that current AI classroom instruction insufficiently foster AI literacy, with only 35.71% addressing higher-level skills such as evaluating and creating AI. AI ethics are even less commonly addressed, featured in just 5.1% of classroom instruction. We classified AI classroom instruction into three categories: conceptual (50%), heuristic (18.37%), and experimental (31.63%). Correlation analysis suggests a significant relationship between the adoption of pedagogical approaches and the development of advanced AI literacy. Specifically, integrating Project-based/Problem-based learning (PBL) with Collaborative learning appears effective in cultivating the capacity to evaluate and create AI. |
| format | Article |
| id | doaj-art-18d958da4abe4e2fa14130866ed5c172 |
| institution | OA Journals |
| issn | 2666-920X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj-art-18d958da4abe4e2fa14130866ed5c1722025-08-20T01:58:16ZengElsevierComputers and Education: Artificial Intelligence2666-920X2024-12-01710029510.1016/j.caeai.2024.100295Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacyDi Wu0Meng Chen1Xu Chen2Xing Liu3The Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, ChinaThe Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, ChinaCorresponding author.; The Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, ChinaThe Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, ChinaThere is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing research on K-12 AI education relies on experiential methodologies and suffers from a lack of quantitative analysis based on extensive classroom data, hindering a comprehensive depiction of AI education's current state at these educational levels. To address this gap, this article employs the advanced semantic understanding capabilities of large language models (LLMs) to create an intelligent analysis framework that identifies learning theories, pedagogical approaches, learning tools, and levels of AI literacy in AI classroom instruction. Compared with the results of manual analysis, analysis based on LLMs can achieve more than 90% consistency. Our findings, based on the analysis of 98 classroom instruction videos in central Chinese cities, reveal that current AI classroom instruction insufficiently foster AI literacy, with only 35.71% addressing higher-level skills such as evaluating and creating AI. AI ethics are even less commonly addressed, featured in just 5.1% of classroom instruction. We classified AI classroom instruction into three categories: conceptual (50%), heuristic (18.37%), and experimental (31.63%). Correlation analysis suggests a significant relationship between the adoption of pedagogical approaches and the development of advanced AI literacy. Specifically, integrating Project-based/Problem-based learning (PBL) with Collaborative learning appears effective in cultivating the capacity to evaluate and create AI.http://www.sciencedirect.com/science/article/pii/S2666920X24000985AI educationLarge language modelsPedagogical approachesAI literacy |
| spellingShingle | Di Wu Meng Chen Xu Chen Xing Liu Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy Computers and Education: Artificial Intelligence AI education Large language models Pedagogical approaches AI literacy |
| title | Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy |
| title_full | Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy |
| title_fullStr | Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy |
| title_full_unstemmed | Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy |
| title_short | Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy |
| title_sort | analyzing k 12 ai education a large language model study of classroom instruction on learning theories pedagogy tools and ai literacy |
| topic | AI education Large language models Pedagogical approaches AI literacy |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X24000985 |
| work_keys_str_mv | AT diwu analyzingk12aieducationalargelanguagemodelstudyofclassroominstructiononlearningtheoriespedagogytoolsandailiteracy AT mengchen analyzingk12aieducationalargelanguagemodelstudyofclassroominstructiononlearningtheoriespedagogytoolsandailiteracy AT xuchen analyzingk12aieducationalargelanguagemodelstudyofclassroominstructiononlearningtheoriespedagogytoolsandailiteracy AT xingliu analyzingk12aieducationalargelanguagemodelstudyofclassroominstructiononlearningtheoriespedagogytoolsandailiteracy |